Brain data
Contents
Brain data¶
This section presents results of brain MRI data. Below are quantitative T1 values computed using the MP2RAGE and the MTsat methods. These values are averaged within the gray matter and white matter masks.
Gray matter qMRI¶
# Python imports
from pathlib import Path
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Get data from github repo
dir = Path('neuromod-anat-brain-qmri')
if dir.exists() is False:
# Get data from github repo
!wget -O neuromod-anat-brain-qmri.zip https://github.com/courtois-neuromod/anat-processing/releases/download/r20210726/neuromod-anat-brain-qmri.zip
!unzip -j neuromod-anat-brain-qmri.zip -d ./neuromod-anat-brain-qmri/
# Read data
df = pd.read_csv("./neuromod-anat-brain-qmri/results-neuromod-anat-brain-qmri.csv", converters={'project_id': lambda x: str(x)})
# Sort data acording to subject and session columns
df.sort_values(['subject', 'session'], ascending=[True, True])
# Define lists for metrics
mean_MP2RAGE_t1_matrix = []
mean_MTS_t1_matrix = []
mean_mtr_matrix = []
mean_mtsat_matrix = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(1, 7, 1):
sub_values = df.loc[df['subject'] == i]
mean_MP2RAGE_ses = []
mean_MTS_ses = []
mean_mtr_ses = []
mean_mtsat_ses = []
for j in range(1, 5, 1):
ses_values = sub_values.loc[sub_values['session'] == j]
MEAN_mp2 = -100
MEAN_mts = -100
MEAN_mtr = -100
MEAN_mtsat = -100
for index, row in ses_values.iterrows():
if row['acquisition'] == 'MP2RAGE' and row['metric'] == 'T1map' and row['label'] == 'GM':
MEAN_mp2 = row['mean']
if row['acquisition'] == 'MTS' and row['metric'] == 'T1map' and row['label'] == 'GM':
MEAN_mts = row['mean']
if row['metric'] == 'MTRmap' and row['label'] == 'GM':
MEAN_mtr = row['mean']
if row['metric'] == 'MTsat' and row['label'] == 'GM':
MEAN_mtsat = row['mean']
# Append values to lists for sessions
mean_MP2RAGE_ses.append(MEAN_mp2)
mean_MTS_ses.append(MEAN_mts)
mean_mtr_ses.append(MEAN_mtr)
mean_mtsat_ses.append(MEAN_mtsat)
# Append session lists to main matrices for each metric
mean_MP2RAGE_t1_matrix.append(mean_MP2RAGE_ses)
mean_MTS_t1_matrix.append(mean_MTS_ses)
mean_mtr_matrix.append(mean_mtr_ses)
mean_mtsat_matrix.append(mean_mtsat_ses)
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
def get_mean(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
mean = float('{0:.2f}'.format(np.mean(mean_list)))
return mean
def get_std(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
std = float('{0:.3f}'.format(np.std(mean_list)))
return std
def get_limit_max(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.max(mean_list) + (np.max(mean_list)-np.min(mean_list))/4
def get_limit_min(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.min(mean_list) - (np.max(mean_list)-np.min(mean_list))/4
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define labels lists (just in case)
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Def global variables for fonts
x_label_tick_font = 13
y_label_tick_font = 13
general_font = 13
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN ------ mp2rage ------ T1
for trace in range(0, len(mean_MP2RAGE_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
showlegend = False,
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ mts ------ T1
for trace in range(0, len(mean_MTS_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = False,
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sup>1</sup> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN ------ nAn ------ MTR
for trace in range(0, len(mean_mtr_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ nAn ------ MTsat
for trace in range(0, len(mean_mtsat_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
# Calculate means
line_1 = get_mean(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- mean
line_2 = get_mean(mean_MTS_t1_matrix) # MTS_t1 --- mean
line_3 = get_mean(mean_mtr_matrix) # MTR --- mean
line_4 = get_mean(mean_mtsat_matrix) # MTsat --- mean
# Add dotted lines for first button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_1]*8,
mode='lines',
name='T<sub>1</sub>(mp2rage)',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_2]*8,
mode='lines',
name='T<sub>1</sub>(mts)',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[3]),
width=2,
dash='dot')))
# Add dotted lines for second button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_3]*8,
mode='lines',
visible=False,
name='MTR',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
# Add dotted lines for thrid button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_4]*8,
mode='lines',
visible=False,
name='MTsat',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
x = [-1, 0, 1, 2, 3, 4, 5, 6]
x_rev = x[::-1]
std_1 = get_std(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- std
std_2 = get_std(mean_MTS_t1_matrix) # MTS_t1 --- std
std_3 = get_std(mean_mtr_matrix) # N/A_mtr --- std
std_4 = get_std(mean_mtsat_matrix) # MTS_mtsat --- std
# Add STD for 1 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_1+std_1]*8+[line_1-std_1]*8,
fill='toself',
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MP2RAGE_t1 STD',
))
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_2+std_2]*8+[line_2-std_2]*8,
fill='toself',
fillcolor='rgba(255, 187, 120,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTS_t1 STD',
))
# Add STD for 2 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_3+std_3]*8+[line_3-std_3]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTR STD',
))
# Add STD for 3 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_4+std_4]*8+[line_4-std_4]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTsat STD',
))
figb.update_layout(title = 'Brain gray matter qMRI microstructure',
updatemenus=[
dict(
active = 0,
x=1.23,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="T<sub>1</sub>",
method="update",
args=[{"visible": [True] + [True]*12 + [False]*12 + [True]*2 + [False]*2 + [True]*2 + [False]*2},
{"yaxis": dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
title='T<sub>1</sub> [s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTR",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*6 + [False]*6 + [False]*2 + [True]*1 +[False]*1 + [False]*2 + [True]*1 +[False]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtr_matrix), get_limit_max(mean_mtr_matrix)],
title='MTR [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTsat",
method="update",
args=[{"visible": [True] + [False]*18 + [True]*6 + [False]*3 + [True]*1 + [False]*3 + [True]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtsat_matrix),get_limit_max(mean_mtsat_matrix)],
title='MTsat [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='T<sub>1</sub> [s]',
yaxis=dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-2.html', config = config)
display(HTML('new-fig-2.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1747/5554310.py:7: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
--2022-05-26 15:55:40-- https://github.com/courtois-neuromod/anat-processing/releases/download/r20210726/neuromod-anat-brain-qmri.zip
Resolving github.com (github.com)... 140.82.113.3
Connecting to github.com (github.com)|140.82.113.3|:443...
connected.
HTTP request sent, awaiting response...
302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/8209c733-4891-4342-8c30-ccd075b128f8?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220526%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220526T155540Z&X-Amz-Expires=300&X-Amz-Signature=e006f6fe9a8b6051e498d56a839dbf08e06185cdf3894d38ec88b69cdfb4234b&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dneuromod-anat-brain-qmri.zip&response-content-type=application%2Foctet-stream [following]
--2022-05-26 15:55:40-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/8209c733-4891-4342-8c30-ccd075b128f8?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220526%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220526T155540Z&X-Amz-Expires=300&X-Amz-Signature=e006f6fe9a8b6051e498d56a839dbf08e06185cdf3894d38ec88b69cdfb4234b&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dneuromod-anat-brain-qmri.zip&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response...
200 OK
Length: 939721 (918K) [application/octet-stream]
Saving to: ‘neuromod-anat-brain-qmri.zip’
neuromod- 0%[ ] 0 --.-KB/s
neuromod-anat-brain 100%[===================>] 917.70K --.-KB/s in 0.04s
2022-05-26 15:55:41 (20.9 MB/s) - ‘neuromod-anat-brain-qmri.zip’ saved [939721/939721]
Archive: neuromod-anat-brain-qmri.zip
inflating: ./neuromod-anat-brain-qmri/results-neuromod-anat-brain-qmri.csv
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White matter qMRI¶
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Read data
df = pd.read_csv("./neuromod-anat-brain-qmri/results-neuromod-anat-brain-qmri.csv", converters={'project_id': lambda x: str(x)})
# Sort data acording to subject and session columns
df.sort_values(['subject', 'session'], ascending=[True, True])
# Define lists for metrics
mean_MP2RAGE_t1_matrix = []
mean_MTS_t1_matrix = []
mean_mtr_matrix = []
mean_mtsat_matrix = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(1, 7, 1):
sub_values = df.loc[df['subject'] == i]
mean_MP2RAGE_ses = []
mean_MTS_ses = []
mean_mtr_ses = []
mean_mtsat_ses = []
for j in range(1, 5, 1):
ses_values = sub_values.loc[sub_values['session'] == j]
MEAN_mp2 = -100
MEAN_mts = -100
MEAN_mtr = -100
MEAN_mtsat = -100
for index, row in ses_values.iterrows():
if row['acquisition'] == 'MP2RAGE' and row['metric'] == 'T1map' and row['label'] == 'WM':
MEAN_mp2 = row['mean']
if row['acquisition'] == 'MTS' and row['metric'] == 'T1map' and row['label'] == 'WM':
MEAN_mts = row['mean']
if row['metric'] == 'MTRmap' and row['label'] == 'WM':
MEAN_mtr = row['mean']
if row['metric'] == 'MTsat' and row['label'] == 'WM':
MEAN_mtsat = row['mean']
# Append values to lists for sessions
mean_MP2RAGE_ses.append(MEAN_mp2)
mean_MTS_ses.append(MEAN_mts)
mean_mtr_ses.append(MEAN_mtr)
mean_mtsat_ses.append(MEAN_mtsat)
# Append session lists to main matrices for each metric
mean_MP2RAGE_t1_matrix.append(mean_MP2RAGE_ses)
mean_MTS_t1_matrix.append(mean_MTS_ses)
mean_mtr_matrix.append(mean_mtr_ses)
mean_mtsat_matrix.append(mean_mtsat_ses)
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
def get_mean(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
mean = float('{0:.2f}'.format(np.mean(mean_list)))
return mean
def get_std(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
std = float('{0:.3f}'.format(np.std(mean_list)))
return std
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define labels lists (just in case)
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Def global variables for font sizes
x_label_tick_font = 13
y_label_tick_font = 13
general_font = 13
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN ------ mp2rage ------ T1
for trace in range(0, len(mean_MP2RAGE_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
showlegend = False,
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ mts ------ T1
for trace in range(0, len(mean_MTS_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = False,
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sup>1</sup> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN ------ nAn ------ MTR
for trace in range(0, len(mean_mtr_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ nAn ------ MTsat
for trace in range(0, len(mean_mtsat_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
# Calculate means
line_1 = get_mean(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- mean
line_2 = get_mean(mean_MTS_t1_matrix) # MTS_t1 --- mean
line_3 = get_mean(mean_mtr_matrix) # MTR --- mean
line_4 = get_mean(mean_mtsat_matrix) # MTsat --- mean
# Add dotted lines for first button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_1]*8,
mode='lines',
name='T<sub>1</sub>(mp2rage)',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_2]*8,
mode='lines',
name='T<sub>1</sub>(mts)',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[3]),
width=2,
dash='dot')))
# Add dotted lines for second button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_3]*8,
mode='lines',
visible=False,
name='MTR',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
# Add dotted lines for thrid button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_4]*8,
mode='lines',
visible=False,
name='MTsat',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
x = [-1, 0, 1, 2, 3, 4, 5, 6]
x_rev = x[::-1]
std_1 = get_std(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- std
std_2 = get_std(mean_MTS_t1_matrix) # MTS_t1 --- std
std_3 = get_std(mean_mtr_matrix) # N/A_mtr --- std
std_4 = get_std(mean_mtsat_matrix) # MTS_mtsat --- std
# Add STD for 1 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_1+std_1]*8+[line_1-std_1]*8,
fill='toself',
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MP2RAGE_t1 STD',
))
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_2+std_2]*8+[line_2-std_2]*8,
fill='toself',
fillcolor='rgba(255, 187, 120,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTS_t1 STD',
))
# Add STD for 2 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_3+std_3]*8+[line_3-std_3]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTR STD',
))
# Add STD for 3 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_4+std_4]*8+[line_4-std_4]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTsat STD',
))
figb.update_layout(title = 'Brain white matter qMRI microstructure',
updatemenus=[
dict(
active = 0,
x=1.23,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="T<sub>1</sub>",
method="update",
args=[{"visible": [True] + [True]*12 + [False]*12 + [True]*2 + [False]*2 + [True]*2 + [False]*2},
{"yaxis": dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
title='T<sub>1</sub> [s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTR",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*6 + [False]*6 + [False]*2 + [True]*1 +[False]*1 + [False]*2 + [True]*1 +[False]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtr_matrix), get_limit_max(mean_mtr_matrix)],
title='MTR [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTsat",
method="update",
args=[{"visible": [True] + [False]*18 + [True]*6 + [False]*3 + [True]*1 + [False]*3 + [True]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtsat_matrix),get_limit_max(mean_mtsat_matrix)],
title='MTsat [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='T<sub>1</sub> [s]',
yaxis=dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-4.html', config = config)
display(HTML('new-fig-4.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1747/43468867.py:5: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display